55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
"""PPO策略快速测试 - 30轮"""
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import yaml
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import numpy as np
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from tqdm import tqdm
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from sumo_vsl_environment import SUMOVSLEnvironment
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from ppo_agent import PPOAgent
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with open("config_sumo_vsl.yaml", "r", encoding="utf-8") as f:
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config = yaml.safe_load(f)
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config["training"]["num_episodes"] = 30
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env = SUMOVSLEnvironment(config)
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agent_config = config.get("agent", {})
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agent = PPOAgent(
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state_dim=env.state_dim,
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action_dims=[5] * env.num_control_zones,
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hidden_layers=agent_config.get("hidden_layers", [256, 256, 128]),
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learning_rate=agent_config.get("learning_rate", 3e-4),
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device=agent_config.get("device", "cuda")
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)
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print(f"开始PPO测试 - 30轮")
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print(f"状态维度: {env.state_dim}, 动作空间: {env.num_control_zones}x5\n")
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try:
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for ep in range(1, 31):
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state = env.reset(seed=42 + ep)
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ep_reward = 0
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done = False
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pbar = tqdm(total=env.episode_length, desc=f"Ep {ep}/30", leave=False)
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while not done:
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action, log_prob, value = agent.select_action(state, deterministic=False)
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next_state, reward, done, info = env.step(action)
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agent.store_transition(state, action, reward, value, log_prob, done)
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ep_reward += reward
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state = next_state
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pbar.update(1)
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pbar.close()
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agent.update(0.0)
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print(f"Ep {ep}: Reward={ep_reward:.2f}, TP={info['throughput']:.0f}, Speed={info['mean_speed_kmh']:.1f}")
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except KeyboardInterrupt:
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print("\n测试中断")
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finally:
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env.close()
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print("\n测试完成！")
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